Financial market data includes information formally generated by a financial exchange that relates to quote and trade activity associated with various financial instruments handled by the financial exchange. Such financial instruments can include, for example, stocks, bonds, derivative products, mutual funds, fixed-income products, or any other financial products bought or sold on the financial exchange. Financial exchanges can include, for example, the New York Stock Exchange (“NYSE”), the NASDAQ Stock Market, and the Chicago Mercantile Exchange (“CME”), among many other financial exchanges.
Financial market data, or market data, typically includes numerous items of information, such as a ticker symbol, bid/ask price, bid/ask size, last price, quote time, exchange identity, and latest volume associated with the symbol, among other possible items. Raw market data generated by a financial exchange is typically placed into some format, and then transmitted by the exchange in a market data feed stream to vendors that collect market data feeds from multiple different financial exchanges. Each financial exchange typically places the market data into a format that is different from the formats of other financial exchanges. For example, a financial exchange in the U.K. might designate a certain symbol for the stock of a particular company, while a financial exchange in the U.S. might designate a different symbol for the same stock. As another example, the language used in the text of a data feed from one financial exchange may be different from the language used in the text of the data feed from another financial exchange. Numerous other significant differences between separate raw market data feeds are known to exist.
A financial services provider, often referred to as a vendor or aggregator, will typically subscribe to and receive financial market data in the form of direct feeds generated by multiple different financial exchanges. Financial services providers can include, for example, Reuters and Bloomberg, among others. Such financial services providers tend to use feed handlers, typically computer servers, that operate to receive, normalize, store, manage and integrate the financial market data received from the multiple exchange feeds. Normalization in this context means that the various formats used by the different financial exchanges to send out their market data feeds are all converted to a single format, such that the data can be more easily used and consumed by a downstream financial institution. The feed handlers will then provide this substantially processed financial data to downstream financial institutions and consumers, such as brokerages.
High frequency trading (“HFT”) refers to certain kinds of financial instrument trading strategies that are characterized by very short transaction times and very short holding periods. Financial institutions running HFT strategies typically implement their strategies on high-speed and highly available computers running on a financial network. The high-speed computers of these financial institutions can often be connected over a financial network directly to the feed handlers of one or more financial services providers and/or directly to one or more market data feeds coming from one or more financial exchanges themselves. In order to execute a HFT strategy well, the overall period of time from receipt of the market data to the execution of a trade is of critical importance. This period of time is often heavily influenced by the length of time it takes for communications to transpire across the network, which is typically referred to as “network latency.”
In order to minimize network latency, vendor feed handling equipment, typically in the form of computer server(s), is generally located as close as possible to the equipment operated by each financial exchange that generates the market data. As more and more vendors (i.e., financial services providers) jockey to locate their own equipment within the limited and finite amount of physical space proximate the equipment of the actual financial exchanges, such prime space becomes increasingly costly and ultimately unavailable.
One solution to this network latency problem can be for a brokerage or other downstream financial institution to subscribe to raw market data feeds directly and to implement its own feed handler functionality on its own high-speed trading platform. While such arrangements can indeed reduce network latency and the overall period of time from receipt of market data to trade execution, the additional costs and complexities can be substantial. Unfortunately, such solutions can require subscribing to many raw market data feeds, as well as implementing and maintaining separately owned systems with feed handlers to process the multiple feeds. As an alternative to the cost and complexity of maintaining their own feed handlers then, many brokerages and other downstream financial institutions outsource this function to the financial services vendors, thus accepting the network latency problems.
Although many systems and methods for conducting market transactions on a financial market network have generally worked well in the past, there is always a desire for improvement. In particular, what is desired are financial data network systems and methods that allow for the communication of financial market data and the execution of market trades with reduced levels of network latency.